Hands-on artificial intelligence education service

ABSTRACT

Indications of sample machine learning models which create synthetic content items are provided via programmatic interfaces. A representation of a synthetic content item produced by one of the sample models in response to input obtained from a client of a provider network is presented. In response to a request from the client, a machine learning model is trained to produce additional synthetic content items.

This application claims benefit of priority to U.S. ProvisionalApplication No. 62/941,559 filed Nov. 27, 2019, titled “Hands-onArtificial Intelligence Education Service,” which is hereby incorporatedby reference in its entirety.

BACKGROUND

In recent years, machine learning has increasingly been used foraddressing problems in a number of domains. The sophistication of themachine learning techniques being used for such tasks as classification,object recognition, fraud detection, and the like has increased to thepoint that it is often difficult even for technically-adept individualsto understand how the techniques work. As a result, the set of qualifiedindividuals who can help high-quality develop machine learning modelsremains relatively small.

In contrast to many traditional machine learning approaches, which maybe referred to as “discriminative artificial intelligence,” the term“generative artificial intelligence” refers to a set of machine learningtechniques that enable human-like creative tasks to be performed withthe help of machine learning models. Whereas traditional discriminativemachine learning typically makes predictions or inferences based onanalysis of input data, humans can collaborate with generativeartificial intelligence tools to create new digital works such as music,art, or even stories. As such, computing systems at which generativeartificial intelligence algorithms are implemented may be consideredpart of the creative process. Applications of generative artificialintelligence include accelerating product development by automaticallyturning rough sketches into images or three-dimensional designs,generating synthetic records which can be used to train other machinelearning models to help diagnose diseases, and so on.

Generative artificial intelligence techniques are typically extremelycomplex, however. They typically require deep knowledge of specializedmethods, and in some cases may require multiple machine learning modelsto be trained together, requiring rare combinations of skills.Individuals interested in becoming fluent in generative and othersimilar artificial intelligence techniques may either have to try tostay abreast of fast moving and often extremely theoretical academicliterature, or delve into online resources which are often dry anddifficult to generalize.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example system environment in which anetwork-accessible artificial intelligence education service may beimplemented, according to at least some embodiments.

FIG. 2 illustrates a high-level overview of an iterative hands-onapproach for learning about artificial intelligence that may be enabledat an artificial intelligence education service, according to at leastsome embodiments.

FIG. 3 illustrates an example of sample building block models which maybe used to illustrate differing levels of complexity of content creationtasks which may be performed using machine learning algorithms,according to at least some embodiments.

FIG. 4 illustrates example generative learning algorithms which may beemployed at an artificial intelligence education service, according toat least some embodiments.

FIG. 5 illustrates an example interactive interface which may be used toexplore details of a generative model, according to at least someembodiments.

FIG. 6 illustrates an example of a tag-based interface which may beutilized to curate training data used for a generative model, accordingto at least some embodiments.

FIG. 7 lists examples of hands-on teaching features which may beimplemented at an artificial intelligence education service, accordingto at least some embodiments.

FIG. 8 illustrates an example technique which may be used to showiteration-to-iteration changes during the training of a generative modelfor creating music examples, according to at least some embodiments.

FIG. 9 and FIG. 10 collectively illustrate examples of programmaticinteractions with an artificial intelligence education service,according to at least some embodiments.

FIG. 11 illustrates an example provider network at which an artificialintelligence education service may be implemented, according to at leastsome embodiments.

FIG. 12 illustrates example domains for which respective generativemodels may be utilized at an artificial intelligence education service,according to at least some embodiments.

FIG. 13 is a flow diagram illustrating aspects of operations that may beperformed at an artificial intelligence education service, according toat least some embodiments.

FIG. 14 is a block diagram illustrating an example computing device thatmay be used in at least some embodiments.

While embodiments are described herein by way of example for severalembodiments and illustrative drawings, those skilled in the art willrecognize that embodiments are not limited to the embodiments ordrawings described. It should be understood, that the drawings anddetailed description thereto are not intended to limit embodiments tothe particular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope as defined by the appended claims. The headings usedherein are for organizational purposes only and are not meant to be usedto limit the scope of the description or the claims. As used throughoutthis application, the word “may” is used in a permissive sense (i.e.,meaning having the potential to), rather than the mandatory sense (i.e.,meaning must). Similarly, the words “include,” “including,” and“includes” mean including, but not limited to. When used in the claims,the term “or” is used as an inclusive or and not as an exclusive or. Forexample, the phrase “at least one of x, y, or z” means any one of x, y,and z, as well as any combination thereof.

DETAILED DESCRIPTION

The present disclosure relates to methods and apparatus for implementinga network-accessible artificial intelligence education service whichprovides a number of easy-to-use hands-on interfaces, enabling users tofamiliarize themselves with and experiment with various aspects ofcomplex machine learning techniques including generative artificialintelligence. As a result of using such a service, individuals withlimited or no prior knowledge of machine learning may soon becomeconversant with artificial intelligence methodologies, and even initiatethe training of customized models which can then be used to producereal-world results. For example, amateur and/or professional musiciansmay be able to quickly generate unique new music of a desired style,visual artists or graphic designers may be able to develop new artwork,video games and the like, and health scientists may be able to generatesynthetic data that can be used to improve disease detection andprediction tools. In at least some embodiments, such an artificialintelligence education service (AIES) may be implemented as part of asuite of services of a provider network. The term “provider network”(sometimes simply called a “cloud”) refers to a large pool ofnetwork-accessible computing resources (such as compute, storage, andnetworking resources, applications, and services), which may bevirtualized or bare-metal. The cloud can provide convenient, on-demandnetwork access to a shared pool of configurable computing resources thatcan be programmatically provisioned and released in response to customercommands. These resources can be dynamically provisioned andreconfigured to adjust to variable load. In some embodiments, a providernetwork may include a machine learning service (MLS) in addition to theAIES, which may be used to help train and/or run models used by the AIESor to provide some of the easy-to-use interfaces used by the AIES. TheAIES may also be referred to as an artificial intelligence educationfacilitation service or an artificial intelligence educationacceleration service, as it may significantly reduce the time taken bylearners to become fluent in various artificial intelligence (AI)techniques.

In some embodiments, the focus of an AIES may comprise one or moreselected sub-fields of machine learning which are exceptionally complex,such as generative artificial intelligence (GAI) techniques. The termsgenerative AI and generative machine learning may be usedinterchangeably herein. Whereas traditional (non-generative) machinelearning typically makes predictions or inferences based on analysis ofinput data, humans may collaborate with GAI tools to create new digitalworks such as music, art, and the like. This creative potential makesGAI an especially attractive entry point to introduce users to machinelearning concepts, as the AIES may be able to demonstrate the benefitsand impacts of various factors by, for example, playing generated musicor displaying generated artwork based on input obtained from users. Inmany of the example scenarios discussed herein, GAI models that cancreate new music derived (at least partly) from short user-providedmusic items created using a simple electronically-connected musicalinstrument (such as a music keyboard connected as a peripheral device toa user's laptop or desktop) are used for educating the users regardingvarious aspects of machine learning. Note that the AIES may utilizeother types of content and/or other types of models as part of itseducational methodology in at least some embodiments; music-producingGAI models represents just one example approach. In order to make iteasy and fun for users to learn about machine learning, the AIES mayrely on a number of sophisticated technical algorithms and techniquesunder the covers in various embodiments—e.g., algorithms that quicklytransform music or other content items to graphical representations thatenable the demonstration of progress being made during model training orthe comparison of different examples, visualization algorithms thatclearly indicate the respective results of multiple stages of a machinelearning pipeline, presentation interfaces that enable easy drill downsinto details of complex neural networks and other models, real-timeif/then comparisons showing the impact of different model meta-parametercombinations or model types, and so on. The complex technical details ofthe underlying algorithms may be kept hidden from the users (at leastinitially); for example, in some embodiments, user may not even have toexamine or modify any source code to generate their own customizedgenerative models. The output items created using GAI tools and modelsmay be referred to as synthetic content items in at least someembodiments.

In embodiments in which music creation is used as the example GAIapplication for acceleration teaching of machine learning concepts,users may be able to utilize either on-screen representations of amusical instrument (e.g., a virtual music keyboard shown on the screen)presented programmatically from the AIES, or connect a simpleelectronically-connected musical instrument to a computing device whichis in turn connected to the AIES via the Internet. AIES users, alsoreferred to as AIES clients, may begin by providing a few notes of musicas input to one or more sample GAI models that create correspondingsynthetic music items, which may be played back to the user to provideimmediate feedback regarding the results achieved using the models.Clients may select or change the preferred genre (e.g., “jazz”, “rock”,etc.) of music of the desired output, and the system may immediatelyplay the synthesized music corresponding to that genre. Clients may viewmeta-parameters and other artifacts of the models being used, and/or thetraining data which was used for the sample models, or even examinewell-commented source code or pseudo-code of the models, if desired. Theclients may eventually become familiar enough to make suggestions, viaeasy-to-use interfaces, to select training data for a new model, and maythen be guided through the training iterations of the model by the AIES.The AIES may provide classification metadata (such as respective tagsand metrics for various music items of a corpus) as well as asophisticated search engine, which can be used by clients to selectexamples for inclusion in a training data set.

In at least some embodiments, to further explicate the training process,the AIES may use sophisticated content transformation techniques topresent visual (or multi-modal) representations of the improvements orchanges in the model as training progresses—e.g., visualsimilarity/difference representations may be generated and presentedwith respect to the output content produced in different trainingiterations. As the clients gain more confidence, they may try changingmeta-parameters (or the training data set) and re-training models. Insome cases, if desired, the clients may view and/or modify source codeof the models to be trained, although accessing the source code may notbe required to train custom versions of the model—e.g., such customversions may be obtained simply by selecting a unique training data setand/or tweaking meta-parameters. Eventually, in at least someembodiments, a client may decide that the quality of the contentgenerated by a particular model trained on behalf of the client is highenough, and the trained model may be stored. In one embodiment, the AIESmay enable clients such as musicians to publish the content generatedusing their versions of the models, e.g., at a web-based music sharingor distribution site, and/or to participate in competitions forcompute-generated content items. The techniques learned by clients usingthe AIES may be transferred to various problem domains in differentembodiments, such as for generating synthetic medical records that canbe used to train disease detection machine learning models, speeding upengineering/design projects by quickly converting rough sketches intoengineering drawings, and so on.

As one skilled in the art will appreciate in light of this disclosure,certain embodiments may be capable of achieving various advantages,including some or all of the following: (a) substantially speeding upthe process of educating interested parties in various aspects ofartificial intelligence and machine learning, by providing hands-ontraining using interfaces that make learning fun; (b) improving theoverall quality of machine learning solutions developed, by rapidlyincreasing the pool of competent practitioners, and (c) by exposing theinternal details of various machine learning models, increasing theoverall trust level of the public with regard to machine learning toolsand applications in general.

According to some embodiments, a system may comprise one or morecomputing devices, e.g., of a network-accessible AIES of a providernetwork. The devices may include instructions that upon execution on oracross one or more processors cause the one or more computing devices toprovide, via one or more interactive programmatic interfaces (such asgraphical or web-based interfaces), an indication of (a) one or moresample models implementing respective generative machine learningalgorithms trained to create synthetic content items of a particularcontent type (such as music, drawing, fiction text or the like) and (b)metadata associated with the one or more sample models, such as thenames of the algorithms used in the models, respective hyper-parametersof the different models, structural details of the models (such as thenumber and types of neural network layers), etc. Respectiverepresentations of (a) a first synthetic content item generated at aparticular sample model (e.g., one of the sample models selected by aclient) in response to a first input content item and (b) a secondsynthetic content item generated at the particular sample model inresponse to a second input content item may be provided, enabling aclient or user to get a high-level sense of the creative capabilities ofthe selected model.

The user may explore various other aspects of the sample models via theprogrammatic interfaces in various embodiments, e.g., experimenting withthe impacts of changing various properties of input content types or themodel hyper-parameters, and may eventually decide to begin the processof training a specific customized model. A plurality of trainingiterations of a custom generative machine learning model may then beconducted, e.g., using resources of a provider network. In someembodiments, the AIES may obtain access to a large corpus of contentitems (e.g., songs and other types of music performances) which can beused to generate tailored training data sets for the generative models.The AIES may implement one or more algorithms to characterize the corpuselements along a plurality of dimensions (e.g., whether and to whatextent a particular musical performance is a “jazz” performance, a“blues” performance, the mood or psychological feelings the musicalperformance is likely to engender in listeners, which instruments orvoice types are used, how many instruments are used, and so on), andproduce classification tags for individual content items using suchalgorithms. Classification tags may also be collected at the AIES fromother sources—e.g., public databases may provide such information as theyear a particular performance was recorded, the country it as recordedin, whether it is part of a soundtrack of a film, and so on, and tagsindicative of such information may also be made available to clients ofthe AIES if desired. A client may submit programmatic requestsindicating which specific tags and/or other metadata of the contentitems of the corpus are to be used to select a training data set for thecustom model in at least some embodiments.

The provider network resources used for training the model on behalf ofthe client may include for example, training resources managed by amachine learning service, one or more software containers of a containermanagement service, compute instances of a virtualized computingservice, storage instances of an object storage service, and so on indifferent embodiments. In at least some embodiments, sophisticatedcontent analysis and transformation tools may be used to convert contentitems of one domain (e.g., music) into objects that are easier tocompare visually (e.g., graphical representations of the music,indicating where a particular music piece lies with respect to typicalrepresentative music pieces of various genres) to help illustrate thechanges in the model as the training iterations proceed. For example,even in the scenario in which the training iterations' input and outputrecords are all music pieces or performances (and are thus notinherently graphical or visual in nature), graphical representations ofthe differences between output produced in one training iteration for agiven input record and another iteration for the same input record maybe presented to the client in at least some embodiments. After thetraining is completed, the trained version of the model may be storedand used to generate new content based on input which was not part ofthe training data set in various embodiments. The AIES may enableclients to save, and generate versions of, content items generated byclient-selected models in at least some embodiments. For example, withrespect to music, one or more editable or read-only file formats (e.g.,mp3, way etc.) may be selected by the clients for the musical itemsproduced by their customized models.

Any of a variety of types of generative machine learning models may beused in different embodiments at an AIES, including but not limited togenerative adversarial networks (GANs), variational auto-encoders(VAEs), auto-regressive models, transformers and the like. According tosome embodiments, a client may be able to try out different model typesfor the same input—e.g., the same set of notes may be provided as inputto a GAN model, a VAE model and so on to see how different the outputsfrom the different models sound. In at least some embodiments, the AIESmay provide not only the values of various model hyper-parameters toclients exploring or experimenting with the models, but also presentsimple graphical interface elements such as sliders, knobs or the likewhich can be used to graphically modify at least hyper-parameters ifdesired for additional training or to execute trained versions of themodels. In some embodiments, the AIES may recommend or suggest changesto try, e.g., based on feedback received from the client regarding thecontent output being generated. For example, the client may indicate,via a programmatic interface, that the pace of a particular output musicpiece is too slow for the client's liking, and the AIES may suggestchanges to one or more hyper-parameters to help speed up the music beingproduced. In at least one embodiment, a client may be provided interfaceelements to switch or substitute between different instrumentcombinations to be used for output music items—e.g., if desired, aclient may be allowed to change the model hyper-parameters (or trainingdata) to include electric guitar sounds instead of acoustic guitarsounds. In some embodiments, the AIES may offer recommendations forcorrecting input provided by a client—e.g., if the client attempts toprovide a well-known melody to be used as input for generating asynthetic music content item, and one or more of the notes provided bythe client differs from an expected note, the AIES may present a messageto the client indicating that the expected note may be preferable, andchange the note if the client agrees. In at least one embodiment, theAIES may offer similar recommendations regarding the output produced bya model trained on behalf of the client—e.g., if the sequence of notesproduced by the model does not meet a euphony or harmony criterion ofthe AIES, the AIES may suggest changes (e.g., to the hyper-parameters ofthe model, or to the output itself) to the client.

Special interactive interfaces for machine learning tasks, referred toas notebooks, may be presented by an AIES in at least some embodimentsto enable exploration, annotation and/or modification of models,training data, and other artifacts. In at least one embodiment, an AIESmay utilize a computing service of a provider network, such as avirtualized computing service or a server-less dynamic provisioningcomputing service implementing a functional programming methodology toimplement such notebook interfaces.

A number of different subjective and/or objective metrics may begenerated for comparing content items produced by a generative model(e.g., in respective training iterations, or in response to differentinput records) at the AIES in some embodiments. In the music domain,generating such metrics may involve, for example, generating structuralsimilarity matrices from music items, identifying the number and typesof instruments used, the number and types of voices (e.g., soprano,baritone etc.) used, the pacing of notes, the ratio of notes thatqualify as belonging to a particular genre such as rock versus jazz,beat patterns of any percussion instruments used, and so on.

In various embodiments, the AIES may provide annotated andthoroughly-commented versions of reference/sample source code of variousmodel and hyper-parameter files, enabling those clients who wish toexamine such artifacts to do so at their leisure. In at least oneembodiment, editing interfaces (e.g., what-you-see-is-what-you-get orWYSIWIG interfaces) may be provided to allow clients to edit source codeor other metadata, and save (or download) their custom versions ifdesired. In one embodiment, the AIES may provide integrations with oneor more code management of software development services orrepositories, e.g., implemented as services of a provider network orInternet-accessible web-sites, at which AIES clients may storecustomized machine learning artifacts generate during theirexperimentation at the AIES. Step-by-step tutorials may be provided bythe AIES for learning about various types of machine learning algorithmsand techniques in some embodiments.

According to at least one embodiment, an AIES client may submit selectedfavorite music items (e.g., compositions from a favorite artist) orother content, and request that the provided content be used to helptrain one or more generative machine learning models. In someembodiments, if/when training examples for a custom version of a modelis to be identified, the AIES may provide a like/dislike interfaceenabling a client to indicate their preferences for AIES-presented musicexamples. The client may listen to each of several songs or music itemsof the AIES's corpus, and provide “like” or “don't-like” feedback abouteach. As the client indicates more preference feedback (i.e., as moredata regarding the likes and dislikes of the client becomes known to theAIES), the AIES may be able to generate a profile of the kinds of musicto be included in the training data set; as such, a training data setbased on the client's preferences may eventually be generated withoutrequiring the clients to use words to describe qualities of the music tobe incorporated in the training data.

Example System Environment

FIG. 1 illustrates an example system environment in which anetwork-accessible artificial intelligence education service may beimplemented, according to at least some embodiments. As shown, system100 includes various resources and artifacts of an artificialintelligence education service (AIES) 110 at which generative AI (GAI)algorithms and models are used to help teach clients via hands-ontechniques about various aspects of artificial intelligence. The AIES110 may implement one or more programmatic interfaces 177 in thedepicted embodiment, such as web-based consoles, graphical userinterfaces and the like, which may be used by clients to interact withthe AIES in various ways. In some embodiments, the programmaticinterfaces 177 may include application programming interfaces (APIs)and/or command-line tools, which may for example be invoked by theweb-based consoles or graphical user interfaces—for example, aninteraction of a client with an element of a graphical user interface orweb-based console may result in the invocation or execution of anunderlying API call.

As an entertaining introduction to various technical aspects of AI, theAIES 110 may provide access to one or more sample GAI models 160 in thedepicted embodiment. For example, in one embodiment, one of the samplemodels may be presented via interfaces 177 as a “rock music” generator,while another may be presented as a “classical music” generator. In onesimple type of interaction, an AIES client may submit a few notes ofmusic as input to a selected sample model 160, and obtain an indication(e.g., a playback) of the output produced by the selected model for thatinput. In at least some embodiments, a music keyboard 151, connected forexample via USB (Universal Serial Bus) or some other peripheralconnector to a client computing device 150A such as a laptop or desktop(which is in turn connected via the Internet or other intermediarynetworks to the AIES) may be used to submit the input for the selectedmodel. Within the AIES, one or more user input collectors 165implemented at one or more computing devices may capture the inputsignals transmitted from the client device, convert/reformat them ifneeded before the sample GAI model is run. The output music generated bythe sample GAI model 160 may be played back, e.g., via one or moreoutput presentation managers 166 and a web browser running at the clientdevice 150A in some embodiments. The client may experiment with othersets of input notes, listen to the different output music items producedin response, and thereby get an introductory high-level sense of whatthe selected sample GAI model does in the depicted embodiment.

Output presentation managers 166 may enable clients, if interested, toview and explore various types of input and other artifacts associatedwith the sample GAI models (and/or other models of interest to aclient). For example, example data records of a corpus 161 (e.g., musicrecordings, drawings etc., depending on the problem domain for which GAImodels are being used) which were used for training and/or evaluatingmodels may be accessed via interfaces 177 in the depicted embodiment,and/or reference artifacts 162 such as commented code orhyper-parameters may be presented to clients if desired. In oneembodiment, the AIES 110 may utilize one or more other services, such asan object storage service of a provider network or a database service ofa provider network, to store the data records corpus 161. In at leastsome embodiments, one or more exploration/editing tools such as machinelearning notebooks may be presented to clients. Notebook interfaces may,for example, automatically incorporate or load various machine learning,statistics and other types of libraries into an interactive developmentand experimentation environment (thereby reducing the amount of workthat has to be done by clients to view or rearrange data sets,experiment with models or data analysis steps and so on), provideeasy-to-use annotation capabilities which clients can use to record andshare comments, suggestions, and the like. The AIES may include one ormore tool managers 163 for managing the underlying resources used forsuch tools in the depicted embodiment.

In at least some embodiments, the AIES 110 may generate and store a setof tags or other similar metadata 171 which can be used to selectsimilar content items from one or more corpuses 161. For example, withrespect to music content items, a number of tags indicative of genres(e.g., “rock”, “jazz”, etc.) may be generated or obtained for respectivecontent items of a corpus 161. With respect to visual artworks, tagssuch as “impressionist”, “medieval”, “cubist”, “abstract expressionist”and the like may similarly be obtained or generated in some embodiments,and so on. In some embodiments, one or more sophisticated contentclassification models may be trained to generate such tags or metadatafor each category of content for which a corpus is managed or stored atthe AIES 110, e.g., using similarity analysis and other algorithms withrespect to well-known examples of the different genres or styles. Inaddition to tags, metadata such as the number of instruments used for agiven music content item, the types of instruments used, and so on mayalso be generated and provided to clients. One or more search engines170 may be configured at or used by the AIES to enable clients to selectdata record examples of their choice for inclusion in training sets,e.g., for retrained versions of the sample GAI models and/or fortraining entirely new models being created on behalf of the clients.

In order to help illustrate the improvements achieved during the courseof iterative training of one or more models to clients via programmaticinterfaces 177, a sophisticated data transformation and visualizationtechnique may be employed at the AIES 110 in some embodiments. Forexample, respective visual representations of output music content items(e.g., produced in respective training iterations for the same inputcontent items) may be generated, structural similarity matrices may begenerated using the visual representations, and/or reduced-dimensionversions of the visual representations may be presented to clients. Anumber of metrics useful to compare content items may be produced bymetrics generators 181 in the depicted embodiment, including for examplestructural similarity/dissimilarity metrics, beat or pace-relatedmetrics, and so on, and presented to clients via interfaces 177. Suchpresentation material may, for example, clearly indicate/illustrate howthe output is changing from one iteration to another, how fast it isconverging on some set of desired attributes, and so on. In someembodiments, a multi-modal presentation of such differences may beprovided—e.g., audio versions of the two output music items may bepresented, compressed or reduced-dimension visual representations (assingle points in a 2-dimensional or 3-dimensional space) of the musicitems may be presented, and/or a video representation showing the musicproperties as a function of time for each of the output music items maybe displayed. For example, if each music item being compared or examinedis 60 seconds long, respective videos or animations that are 60 secondslong may be generated and shown in some embodiments.

Clients may select training data sets and request the training of theirown models (e.g., variants of the sample GAI models, or entirely newmodels coded by the clients) via programmatic interfaces 177 in thedepicted embodiments. Models implementing a variety of GAI algorithms131 of the AIES's algorithm library 130 may be used as sample models (orfor client-requested custom models) in the depicted embodiment,including for example generative antagonistic network (GAN) algorithms133, variational auto-encoders (VAE) algorithms 134, auto-regressive(AR) models 135 and the like. In some embodiments, the AIES may maintaina trained model repository 120 at which trained (or re-trained) versionsof a client's customized models and/or the sample GAI models may bestored; in other embodiments, external repositories may be utilized.Models may be trained at client request using a set of model trainingresources 122 in the depicted embodiment. After an acceptable trainedversion of a model is generated and stored, in some embodiments aseparate set of model execution resources 140 may be employed to run themodel on new inputs (e.g., inputs which were not part of the trainingdata set of the model). In at least one embodiment, a machine learningservice of a provider network may be employed to train and/or run modelson behalf of the AIES, and/or to store the trained models.

In some embodiments, in addition to or instead of using music-relatedGAI models for educating clients, an AIES may use GAI models for othertypes of content creation or synthesis. For example, in one embodiment,a client may use a tablet based drawing or painting interface 179 tosubmit visual content items (e.g., sketches of landscapes) via a clientcomputing device 150B, and GAI models that generate new visual contentitems based on the supplied input may be used to illustrate the conceptsof machine learning, using techniques analogous to those discussed abovein the context of music.

Overview of Example Hands-On Learning Experience

FIG. 2 illustrates a high-level overview of an iterative hands-onapproach for learning about artificial intelligence that may be enabledat an artificial intelligence education service, according to at leastsome embodiments. To begin with, a client of an AIES similar in featuresand functionality to AIES 110 of FIG. 1 may participate in one or morepreliminary familiarization sessions 202 in which the baseline conceptsof generative AI are presented by the AIES. In such sessions, forexample, an input music item created by a client (or selected from amenu of available simple tunes) may be provided as input to one or moresample, pre-trained GAI models, and the corresponding output generatedby the GAI model may be presented to the client. This may indicate thetypes of operations a GAI model typically performs: it learns (duringtraining) various salient characteristics of a collection of inputrecords, and is then able to create or synthesize new output recordsbased on (a) the learned aspects of the training data and (b) thecharacteristics of the input records fed into the models, with somelevel of randomization being introduced to prevent repeat creation ofidentical output even for the same input record.

In a second stage, after the client has participated in the simple inputversus output experiments of the initial familiarization sessions, aclient may perform artifact exploration 206 using AIES interfaces insome embodiments. In this phase of the process, the client may wish totake a look at such things as the type and structure of the model (e.g.,are GANs or VAEs used, are neural networks used, how many neural networksub-models are used, etc.), significant hyper-parameters (e.g., how manyneural network layers of various kinds are used, what factors are usedto end training iterations, etc.) and their values and so on.

In an experimentation stage 210, a client may start trying outvariations in various properties of the GAI system—e.g., to obtainanswers to questions similar to “what happens if I change thehyper-parameters or input slightly?” In at least some embodiments,compelling visualizations, including videos, multimedia and/or stillgraphics, may be generated to illustrate answers to such types ofquestions. Structural similarity matrix-based visualizations of the kinddiscussed above may be employed in some embodiments to help provide moreintuitive or easy-to-grasp explanations of differences (e.g., betweenthe outputs generated using different inputs) in some embodiments.

Some clients may eventually gain enough conference during the firstthree stages of the approach shown in FIG. 2 to begin to try their handsat generating custom models. As part of hands-on data set and modeldevelopment stage 214, such clients may, for example, decide to createcustom training data sets by filtering a corpus of examples using tagsor other metadata provided by the AIES, modify model code orhyper-parameters, and so on. Clients may go back and forth between thedifferent stages shown in FIG. 2 if desired in various embodiments;e.g., a client may go through all one or more of the stages shown inFIG. 2 stages for GAN models, and then decide to repeat some of thestages for VAE models.

Example of Simple Building-Block Models for Music Generation

FIG. 3 illustrates an example of sample building block models which maybe used to illustrate differing levels of complexity of content creationtasks which may be performed using machine learning algorithms,according to at least some embodiments. In the depicted embodiment, asuite 300 of three GAI models for synthetic music creation, calledmodels A, B and C respectively, may be presented via programmaticinterfaces to an AIES client.

Example generative model A may consume sequences of a few musical notes302 each as input, e.g., including randomly selected notes produced by agiven instrument such as a keyboard of the kind discussed above, andcreate a short musical melody 312 (using the same instrument) based onthe input notes. The overall duration of the output melody may beapproximately equal to the overall duration of the input notes sequence.

Example generative model B may take such short melodies 312 as input,and generate respective extended melodies 322 as output (e.g.,generating 60-second output items corresponding to 30-second inputitems), without adding any additional instrumentation or accompanimentin the depicted embodiment.

Example generative model C may take an extended melody 322 as input, andgenerate a multi-instrument music item 332 as output, adding some numberof instruments as accompaniments in the depicted embodiment. The threebuilding-block models may represent different levels of algorithmcomplexity in the depicted embodiment—for example, model C may beconsidered the most complex and model A the least complex. Clients of anAIES may examine details of the individual models to appreciate theunderlying structural and/or algorithmic differences that enable suchdifferences in complexity in various embodiments. In at least someembodiments, combinations of the building block models A, B and C may beexecuted as a pipeline, and the AIES may enable clients to construct andexperiment with such pipelines instead of working with just one of themodels at a time. Thus, in such an embodiment, an interactive graphicalinterface may be provided enabling a client to link and experiment withmodel A and model B as a pipeline, model A and model C as a pipeline, orthe combination of models A, B and C as a pipeline. Synthetic outputproduced at each of the stages of a pipeline may be compared by theclient using the AIES interfaces. The building-blocks pipelines may thusenable clients to gain an understanding and appreciation of the way inwhich different machine learning models are often combined in practice.

Example Generative Learning Algorithms

FIG. 4 illustrates example generative learning algorithms which may beemployed at an artificial intelligence education service, according toat least some embodiments. As shown, algorithms and model types 400 mayinclude, among others, generative adversarial networks (GANs) 412,variational auto-encoders 422, autoregressive models 432 and transformermodels 442 in the depicted embodiment.

A GAN 412 may comprise two underlying neural networks in variousembodiments, known as the generator (or composer) and the discriminator(or judge). The generator attempts to create examples of data that arerealistic, while the discriminator attempts to distinguish between thegenerated examples and a set of real examples. The generator and thediscriminator are trained together, with the generator in effect tryingto convince the discriminator that the generator's output representsreal examples, and the discriminator making judgements regarding thefidelity of the composed examples. The two networks attempt to each anequilibrium during training, such that data produced by the generatoreventually reaches a threshold level of similarity with respect to thereal data.

A VAE 422 may also include two underlying networks, an encoder and adecoder, each of which may also be implemented using neural networks insome embodiments. The encoder learns to construct a latent distributionrepresenting characteristics of the input data records, which is thensampled and passed to the decoder to generate output data records whichincorporate learned features of the input which were captured in thelatent distribution. By identifying specific properties of theintermediary latent distributions which correspond to specific outputcharacteristics (such as, in the case of portrait photographs, thepresence of a feature such as a smile on the face of an individualrepresented in the portrait), and sampling or modifying the latentdistribution appropriately, the output generated by the decoder may bemodulated or controlled in at least some embodiments. For example, byadjusting aspects of the latent distribution or its sample, an outputgroup photograph with more smiling faces than the input may begenerated. Similarly, for music items, properties such as “jazziness”may be increased using analogous adjustments in some embodiments.

Autoregressive models 432 may have a simple and stable training processand may produce good results with the help of efficient sampling of theinput data. Autoregressive models may implicitly define a distributionover sequences using the equivalent of the chain rule for conditionalprobability, whereby in each step the distribution of the next sequenceelement is predicted given the previous elements. Autoregressive models432 may be considered representations of random processes whichcorrespond to time-varying processes in the real world.

Transformer models 442 may combine convolutional neural networks withattention mechanisms (e.g., self-attention models) to generate contentitems in various embodiments (e.g. to generate output text from inputtext). The attention mechanisms, as suggested by the name, enable themodel to focus on the more relevant parts of the input records. In someimplementations, a transformer may include multiple encoders anddecoders, with each encoder and each decoder in turn comprising one ormore attention layers and a feed forward neural network.

Other types of generative algorithms and models than those shown in FIG.4 may be employed in some embodiments at an AIES. In at least someembodiments, less complex neural network-based models than those shownin FIG. 4 , e.g., models utilizing various types of recurrent neuralnetworks, LSTM (Long Short Term Memory) units and the like may also beemployed for generating some types of content (e.g., text). At least onemodel that does not utilize neural networks may be used in oneembodiment.

Example Interface to Explore Details of a Model

FIG. 5 illustrates an example interactive interface which may be used toexplore details of a generative model, according to at least someembodiments. In the depicted embodiment, a representation of agenerative adversarial network (GAN) model (which may be one of thesample GAI models of the AIES, or a custom model created on behalf of aclient of the AIES) is presented to a client via exploration interface500. Similar interfaces may be implemented to allow AIES clients toexplore other types of GAI models in different embodiments, includingVAEs, auto-regressive models and/or transformer models.

The interface 500 may include a model structure overview diagram 591 aswell as several interface elements which can be used to view and/ormodify additional details regarding various aspects of the model and thedata being used. The model comprises a generator neural network 510 anda discriminator neural network 512. Inputs to the generator neuralnetwork 510 include random latent variables 514 (sometimes referred toas a “z vector”) and feedback based on loss functions 517 computed onthe output of the discriminator neural network 512. Input of thediscriminator neural network 512 comprises samples of the output of thegenerator 510 as well as real example records 505. The discriminator 512attempts to distinguish between the real example records and thoseproduced by the generator, as indicated in the “real or fake?” element515.

A client of the AIES may drill down into various aspects of the model inthe depicted embodiment via respective elements of the interface 500.For example, interface element 551 may be used to access (e.g., view orhear, depending on the nature of the data) one or more real examplerecords 505, while interface element 552 may be used to access one ormore synthetic records produced by the generator. The view generatordetails interface element 553 may allow a client to inspect the internalstructure (e.g., the number and types of neural network layers) of thegenerator 510, and the view discriminator details interface element 554may enable the exploration of the internal structure of thediscriminator (as well as loss function(s) 517). The viewhyper-parameters details interface element 555 may enable the client toview values of (and explanatory comments regarding) varioushyper-parameters 592 of the model in the depicted embodiment. In atleast some embodiments, clients may be able to obtain details regardingvarious parts of the model (e.g., the discriminator or the generator)simply by clicking on the images or icons representing the parts. In atleast one embodiment, in addition to allowing clients to view details,the interface 500 (or other interfaces invoked from interface 500) mayenable clients to change or edit various aspects of the model: forexample, slider interface elements or knob interface elements formodifying hyper-parameter values may be provided as part of a responseto a request for hyper-parameter details. Clients may, for example,modify the number of artificial neurons in various layers of the model,and/or the number and types of the layers themselves. Clients may beable to create and save a new version of the model, if desired, whichincludes the modifications.

Example Tag-Based Interface for Data Set Selection

FIG. 6 illustrates an example of a tag-based interface which may beutilized to curate training data used for a generative model, accordingto at least some embodiments. In at least some embodiments, a client ofan AIES similar in features and functionality to AIES 110 of FIG. 1 maywish to train a new generative model to produce content items which havea selected combination of properties (e.g., music items in which drumsdominate, but violins are also present) which differ from the propertiesof the output of the sample models of the AEIAS. In order to obtain suchcustomized output characteristics, a training data set which comprisesexamples with similar characteristics may have to be obtained. In thedepicted embodiment, the AIES may present a tag-based filteringinterface 600 to enable the client to obtain a customized training dataset. The example tags shown are for music items; other combinations oftags may be obtained, generated and presented analogously for othertypes of content such as paintings, drawings and the like in differentembodiments.

Generally speaking, the ability to curate datasets that accuratelyrepresent the type of data or content to be generated is an essentialaspect of using generative models. In various embodiments, an AIES maycollect metadata that can be used to characterize example items of oneor more corpuses (e.g., a music corpus, a painting corpus, a text corpusetc.) from a variety of sources including public databases accessiblevia the Internet, and generate tags based on the collected metadata.Indexes on the collected metadata may be created and utilized for anefficient search engine of the AIES in the depicted embodiments, e.g.,with the search engine and indexes utilizing one or more services of aprovider network (such as a search service, a storage service, variouscomputing services and the like). In some embodiments, the data items ofthe various corpuses may be analyzed using other machine learning modelsto generate at least some of the metadata. With respect to musical data,structural information obtained about the music items may include suchproperties as tempo, number of instruments, types of instruments, andthe like, which may also be mapped to tags selectable via an interfacesimilar to interface 600.

Tags for a number of different 605 genres of music may be selectable bya client in the depicted embodiment, including for example “Classical”,“Rock, “Jazz”, “Pop”, “Hip-Hop” and the like. Tags for popular artists625 or musical groups may be displayed and selected via interface 600,such as Artists A, B, C and D. Clients may request that examples ofmusic with dominant instruments 645, such as guitar, drums, violin,trumpet and the like be selected from the available corpus for inclusionin a training data set in at least some embodiments. In someembodiments, temporal tags 635, corresponding for example to respectivedecades in which the music items were produced (such as 2010 s, 1960 setc.) may be presented as options for selecting examples for thetraining data. Clients may select multiple tags for the filtering to beperformed on their behalf in at least some embodiments. In the depictedembodiment, only a few example tags for a given tag category (e.g.,genre, artist, etc.) are shown; if the client does not find theparticular tags of most interest, interface elements such as the “More .. . ” buttons shown in FIG. 6 may be employed to view additional tags ofvarious categories. In some embodiments, a pool of annotators may beutilized to generate at least some of the tags which can be used tofilter data sets. In one embodiment, a network-accessible service of aprovider network may provide programmatic interfaces which can be usedby individuals to register as annotators, and the AEIAS may use such aservice to help generate tags for its corpuses of music and other typesof content. As mentioned earlier, in some embodiments a preferencededuction approach (in which a client provides feedback regardinglikes/dislikes for specific examples of music or other contents, insteadof or in addition to selecting tags, thereby enabling the AIES to deducethe kinds of music the client wishes to include) may be used to selecttraining data.

Example Hands-on Teaching Features

FIG. 7 lists examples of hands-on teaching features which may beimplemented at an artificial intelligence education service, accordingto at least some embodiments. Generative models that produce music itemsas output, derived at least in part from client-provided input musicitems, may be used to help teach AIES clients about machine learning inthe depicted embodiment. One of the key hands-on aspects or features 700of the AIES is the real-time audio playback of generated music examples702. This provides immediate feedback to the clients regarding thecreative transformations of the models being used, which may help retainthe interest of AIES clients.

Clients may be afforded the opportunity to dynamically switch betweenmodel types 704 via the interactive interfaces of the AIES in someembodiments. For example, a client may generate an input set of notes,hear the corresponding output generated by a GAN model, and then use asingle click on a web page to hear the corresponding output (for thesame input) generated by a VAE model or a transformer model. Perceiveddifferences between the outputs produced by the different model typesmay entice the client to further investigate the details of the modeltypes. Clients may similarly be provided easy-to-use interfaces able todynamically switch between models (such as the building block modelsdiscussed earlier) of different level of complexity, and/or combinevarious models in a pipeline or workflow in various embodiments.

If and when a client wishes to explore the influence of varioushyper-parameter values on the behavior or output of a model, graphicalselection of combinations 706 of different values may be enabled by theAIES in at least one embodiment. For example, graphical user interfaceelements such as sliders, knobs and the like, displayed for severaldifferent hyper-parameters on the same web page or layout, may be usedto change hyper-parameter values singly, or to try out new combinations.

In at least some types of GAI models, the output may be based not onlyon the input and the model parameters learned during training, but alsoon a randomization factor (or factors), sometimes referred to as a “zvector”. Such randomization may lead to the desirable feature that evenif the same input record is supplied repeatedly to the model, theoutputs produced for the different repetitions is not identical. In someembodiments, clients may be allowed to experiment with suchrandomization inputs 708 via graphical user interface elements, e.g., toincrease or decrease the extent of random variation introduced into themodel outputs.

Graphical interfaces for viewing model components/structures 710 may beprovided by the AIES, similar for example to the interface discussed inthe context of FIG. 5 for a GAN model. Generally speaking, suchinterfaces may enable clients to obtain any of multiple levels of detailregarding various aspects of the models, without having to open editingtools or the like.

In at least some embodiments, visual representations of a number ofmetrics computed primarily or specifically to help educate clients maybe presented by an AIES. Such metrics may, for example, includesimilarity/difference metrics 712 between various content items, such asthe content items generated in different training iterations of themodel for a given input. In the case of music items, a multi-steptransformation operation may be performed to generate such visualsimilarity metrics, as discussed in further detail below in the contextof FIG. 8 . Such visual representations may help make it easier forclients to appreciate the improvements achieved over multiple trainingiterations, and thus help illustrate the benefits of iterative training.

Fully-commented and easy to read versions of artifacts such as sourcecode, hyper-parameter files, tutorials and the like may be presented aspart of the hands-on education for clients in various embodiments, asindicated in element 714. In addition, in at least some embodiments,simplified machine learning notebook interfaces 716 may be presented toclients for exploring/annotating models and data. Some of the overheadassociated with setting up such notebooks, such as acquiring computeresources and/or software containers, may be eliminated to reduce theburden on the clients (such tasks may be performed transparently, behindthe scenes, by the AIES). Other hands-on teaching features andtechniques may be employed at an AIES in some embodiments, differentfrom those shown in FIG. 7 .

Example Visual Representations of Iterative Training Improvements

FIG. 8 illustrates an example technique which may be used to showiteration-to-iteration changes during the training of a generative modelfor creating music examples, according to at least some embodiments.Respective output music files 801A and 801B may be produced (e.g., forthe same input file) in iterations K and (K+1) of the training of a GAImodel in the depicted embodiment. Generally speaking, the patterns ofmusic in the files 801 may be similar if the music belongs to the samegenre. One or more transformation functions 810 may be applied togenerate intermediary graphical representations 811 of the respectivefiles, e.g., in a format similar to a piano roll. A piano roll is amusic storage format in which, for example, perforations are generatedon a sheet or roll of paper to indicate note control data. The roll canbe moved over a reading system known as a ‘tracker bar’ and the playingcycle for each recorded musical note may be triggered (e.g., at aplayback piano or reproducing piano) when a perforation crosses the barand is read. Note that at least in some embodiments in which piano-rolllike representations of the music are generated, they may be createddigitally, without using physical paper. Other types of intermediarygraphical representations may be generated in different embodiments,which may not necessarily be based on piano rolls.

A respective structural similarity calculation 820 may then be appliedto the respective intermediary graphical representations 811 to generatesimilarity information (e.g., such as similarity matrices 822) for thepair of music files 801 in the depicted embodiment. Any of a number ofstructural similarity determination algorithms may be used in differentembodiments; in some embodiments, a perception-based model thatconsiders image degradation as perceived change in structuralinformation, while also incorporating important perceptual phenomena,including both luminance masking and contrast masking terms may beemployed. A visualization technique for similarity data (e.g.,multi-dimensional scaling 830) may be applied to the similarity matrices822 to generate respective visualizations 840 indicatingsimilarities/differences among the music files 801. In multi-dimensionalscaling, an input matrix indicating dissimilarities between pairs ofitems is analyzed, and a coordinate matrix is produced as output, inwhich a loss function referred to as strain may be minimized.Combinations of other techniques may be employed in some embodiments.

In the depicted example scenario, points 836 may represent theindividual music files 801, shown in relation to genre representationpoints 835. One of the points 835 may, for example, represent “1970sjazz from the USA”, while another may represent “1960's Brazilian jazz”.The relative proximity or distance of the output representation point836 from these genre points may indicate how close the output is toreplicating or emulating key characteristics of the genres. The movementor repositioning of the training output representation points 836 fromiteration K to iteration (K+1) may indicate the different levels oflearning achieved in the respective iterations, for example. In someembodiments, the AEIAS may indicate the repositioning even moreconcretely, e.g., by showing an animation illustrating the movement ofthe training output representation. In one embodiment, visualrepresentations of the structural similarity matrices 822 (e.g., in theform of heat maps) may also or instead be presented to clients of theAIES. Other types of visualizations indicating similarities ordifferences between content items may be generated in other embodiments.The kinds of visualizations shown on FIG. 8 may be utilized at an AIESin some embodiments not just for indicating differences between outputsof different training iterations, but also to compare input contentitems, or output items generated after the training is concluded. Inaddition to visualizations such as those shown in FIG. 8 , in at leastsome embodiments audio playbacks which emphasize the differences betweenrespective music items may be provided—e.g., the AIES may, if desired,play back the sub-passages that are the most different between the musicitems being compared. The AIES may detect such “more different” passagesusing a number of different metrics computed for the music items, suchas the instrument combination, the pitch, the tempo, etc. of 5-secondlong passages.

Example Programmatic Interactions

FIG. 9 and FIG. 10 collectively illustrate examples of programmaticinteractions with an artificial intelligence education service,according to at least some embodiments. In the embodiments depicted inFIG. 9 and FIG. 10 , an AIES 991, similar in features and functionalityto AIES 110 of FIG. 1 , may implement one or more programmaticinterfaces 977, such as graphical user interfaces, web-based consolesand the like, which may be used by clients to submit requests andreceive corresponding responses. Such interfaces may include elements(such as clickable links, buttons, sliders and the like) which may inturn cause lower-level programmatic interfaces such as APIs orcommand-line tools to be invoked. Generally speaking, individual ones ofthe interactions illustrated in FIG. 9 and FIG. 10 may be implementedusing either the higher-level graphical or web-based interfaces, or thelower-level APIs/commands in different embodiments. In some cases,simply accessing a web-based console or portal may cause severallower-level APIs to be invoked.

A ShowSampleModelList request 905 may be submitted to AIES 991 by aclient 901 via programmatic interfaces 977 in the depicted embodiment todetermine the kinds of models available for familiarization with variousAI techniques. In response, a set of sample models may be indicated,e.g., via one or more SampleModels messages 907. In some embodiments,the client 901 may then select one of the sample models for furtherinteractions, and submit client-created input for the model via anInputForSelectedSampleModel message 910. The input provided by theclient may be fed to the selected sample model, and the output producedmay be presented (e.g., via audio playback in the case of musical items)in one or more OutputOfSampleModel messages 912.

In some embodiments, the AIES 991 may provide access to one or morewell-documented easy-to-run tutorials regarding the sample models and/orother aspects of artificial intelligence, and a client 901 may submit aprogrammatic request 920 to start a selected tutorial. A separateinterface for the tutorial may be generated in response at the AIES 991,and presented in the form of one or more Tutorial Step View responses922.

If and when a client wishes to view details regarding one or moremodels, a ViewModelArtifacts request 924 and/or a ViewModelStructurerequest 928 may be submitted in various embodiments. In response, theAIES may provide information about (or links to) artifacts such assource code, hyper-parameter names and settings, etc., in one or moreModelArtifacts messages 926 and/or provide details about model structurein one or more ModelStructureInfo messages 929.

In at least some embodiments, a client may wish to change one or moresettings of a model (e.g., a particular hyper-parameter value), andobserve the resulting changes in output produced by the model. AChangeModelSettings message 931 may be submitted indicating desiredmodifications to the settings, and the AIES 991 may provide feedbackregarding the impact of the changes in one or more ChangeImpact messages933. Note that depending on the setting which was changed, thecomplexity of the model and/or the stage (e.g., training versuspost-training) of the development of the model, it may take some timefor the AIES to determine/detect the impact of the changes, so theChangeImpact messages 933 may not necessarily be provided immediately.Other impact changes may be determinable in real-time in at least oneembodiment, so the corresponding ChangeImpact responses may benear-instantaneous.

A request to create a filtered or curated training data set from alarger corpus of examples may be submitted via a FilterTrainingDatarequest 934 in some embodiments, Such a request may, for example,indicate some client-selected combination of AIES-provided tags of thekind discussed earlier, the number of desired example records, and soon. The AIES may utilize a search engine to quickly generate therequested training data set, and provide an indication of the results ofthe search via one or more FilteredDataSet responses 935 in someembodiments.

Training of a model implementing a client-selected machine learningalgorithm may be initiated in response to a StartTrainingIterationsrequest 1005 (shown in FIG. 10 ) in some embodiments at the AIES 991. AnIterationsStarted message 1007 may be sent to the client, and in someembodiments a dynamically updated view of the training iterations may bepresented (e.g., the iteration count may be displayed, visualizations ofthe output produced for a given input record may be presented for eachiteration, and so on. Clients 901 may pause the training iterations,e.g., to examine details of the progress achieved thus far, bysubmitting PauseTraining requests 1010 in the depicted embodiment. ATrainingPaused response 1012 may be sent to the client after thetraining operations are paused.

Clients may obtain one or more metrics pertaining to a selected pair oftraining iterations (which may not necessarily be consecutiveiterations) by submitting a CompareIterations request 1020 in thedepicted embodiment. Any of a number of metrics may be provided inresponse, e.g., in the form of one or more ComparisonMetrics messages1022 in the depicted embodiment. In some embodiments, both objective andsubjective metrics may be provided, e.g., using visualizations similarto those discussed earlier. Objective metrics may rely on facts or knownproperties which can be easily quantified. For example, in music, suchmetrics may include to drum patterns, percentages of various pitches ina music item, percentages of empty notes etc. These can be objectivelymeasured and by showing that the more recently generated data is gettingcloser to the desired type of data in terms of these objective metrics,it may be shown that the model is working as desired in variousembodiments. In image processing related domains, properties likegradient histograms, image intensity, number of prominent corners in theimage and the like may be used as objective metrics useful for comparingpairs of data items. Subjective metrics such as the “jazziness of music”or “beautiful sceneries in an image” may be harder to quantify, althoughsuch properties are relatively easily understood by humans. By usingtechniques such as the kinds of tag-based filtering discussed above,datasets that closely represent such subjective qualities may beobtained by AIES clients and used to train models. Then, by measuringthe similarity of the generated data against such datasets, e.g., usingthe structural similarity and multi-dimensional scaling techniquesdiscussed in the context of FIG. 8 , subjective metrics may also beproduced and used for indicating comparisons between iterations (orbetween data items in general).

A client 901 may submit requests to save one or more models (e.g.,models being trained on the client's behalf) via SaveModel requests 1024in the depicted embodiment. The model and its associated artifacts(hyper-parameter files, training data set, etc.) may be saved atpersistent storage devices of a repository in the depicted embodiment,and a Model Saved response message 1026 may be sent to the client. Insome embodiments, the model and its artifacts may be stored in the formof a software container managed by a container service of a providernetwork, which can then be easily used to run the model at any ofvarious computing devices. Clients may issue requests to run savedmodels using RunModel requests 1028, and receive the output generated bythe models for various input records via ModelOutput messages 1029.

In some embodiments, an AIES client 901 may submit new examples ofcontent items to be included in a corpus or training data set via one ormore AddExampleToDataSet requests 1031. The supplied items may be addedto the targeted data set and a DatSetModified message 1033 may be sentas an acknowledgement in at least some embodiments.

According to some embodiments, a client 901 may wish to publish contentitems generated using a model customized by the client, e.g., to one ormore music streaming web sites, video streaming web sites or the like. APublishGeneratedOutput request 1034 may be sent, indicating thegenerated content and the targeted destinations to which the contentshould be published. The AIES may facilitate the publishing and return aPublishDone message 1035 in some embodiments. In one embodiment, theAIES may provide a competition interface which can be used by clients tosubmit generated content items (e.g., along with some information aboutthe models used), and the items generated by the participants in thecompetition may be rated or ranked by other users. Other types ofprogrammatic interactions, not shown in FIG. 9 or FIG. 10 , may besupported by an AIES 991 in various embodiments.

Example Provider Network

As mentioned earlier, in at least some embodiments, an AIES similar toAIES 110 of FIG. 1 may be implemented as part of a suite of services ofa provider network or cloud computing environment. FIG. 11 illustratesan example provider network at which an artificial intelligenceeducation service may be implemented, according to at least someembodiments. Networks set up by an entity such as a company or a publicsector organization to provide one or more network-accessible services(such as various types of cloud-based computing, storage or analyticsservices) accessible via the Internet and/or other networks to adistributed set of clients may be termed provider networks. A providernetwork may sometimes be referred to as a “public cloud” environment.The resources of a provider network may in some cases be distributedacross multiple data centers, which in turn may be distributed amongnumerous geographical regions (e.g., with each region corresponding toone or more cities, states or countries. For example, a cloud providernetwork can be formed as a number of regions, where a region is ageographical area in which the cloud provider clusters data centers.Each region can include two or more availability zones connected to oneanother via a private high speed network, for example a fibercommunication connection. An availability zone refers to an isolatedfailure domain including one or more data center facilities withseparate power, separate networking, and separate cooling from those inanother availability zone. Preferably, availability zones within aregion are positioned far enough away from one other that the samenatural disaster should not take more than one availability zone offlineat the same time. Customers can connect to availability zones of thecloud provider network via a publicly accessible network (e.g., theInternet or a cellular communication network).

In the embodiment depicted in FIG. 11 , provider network 1101 includes avirtualized computing service 1103, a database management service 1143,a server-less dynamically provisioned computing service 1153, a parallelcomputing service 1113, an object storage service 1133, a streaming datamanagement service 1173 that includes one or more data ingestionmanagers 1176 and one or more store managers 1179, as well as a machinelearning service 1123 and an AIES 1163. AIES 1163, which may includenumerous computing and/or storage devices implementing samplemodels/artifacts 1194, metrics generators 1196, search engine 1195, datapresentation managers 1197 as well as other components similar to thoseshown in AIES 110 of FIG. 1 , may utilize the machine learning service1123 extensively in the depicted embodiment. For example, the trainingor re-training of GAI models used as sample models, the execution oftrained versions of the models, and so on, may be conducted using MLS1123 resources including model training managers 1171 and modelexecution managers 1172.

Each of the services of provider network 1101 may include a respectiveset of computing devices and/or other resources in some embodiments.Components of a given service may utilize components of other servicesin the depicted embodiment—e.g., compute instances (CIs) 1109A or 1109B(such as guest virtual machines) set up at the virtualization hosts 1108of the virtualized computing service 1103 and/or storage servers 1135 ofan object storage service 1133 may be employed by various other servicesof provider network 1101 to implement their respective functions.Individual ones of the services shown in FIG. 11 may implement arespective set of programmatic interfaces 1177 which can be used byexternal and/or internal clients (where the internal clients maycomprise components of other services) in the depicted embodiment.Individual ones of the services shown in FIG. 11 may each provide highlevels of automated scalability, availability, data durability, andfailure resilience, enabling varying levels of workloads to be handledgracefully.

AIES 1163 may interact with or utilize other services of the providernetwork 1101 in one or more ways in some embodiments. At least somecomponents of the AIES may utilize components of other services in oneembodiment—e.g., search engine 1195 may comprise one or more computeinstances 1109 or compute clusters 1115 of parallel computing service1113, corpuses of content items, search indices and other data may bestored at database servers 1145 or storage servers 1135, machinelearning notebooks and visualization data sets may be prepared usingresources of server-less dynamically provisioned execution resourcespool 1155, and so on. In at least one embodiment, at least some of thetechniques discussed above for accelerating/facilitating AI educationusing hands-on approaches may be implemented without acquiring resourcesof network-accessible services such as those shown in FIG. 11 . Forexample, a standalone set of computing devices which are not part of anetwork-accessible service may be used in some embodiments.

Example Problem Domains

FIG. 12 illustrates example domains for which respective generativemodels may be utilized at an artificial intelligence education service,according to at least some embodiments. Music composition 1204 has beendiscussed extensively as one example domain herein. GAI models thatgenerate new or synthetic music items corresponding to input musicpassages or notes, based on the characteristics of the training data setused and/or randomized input, may represent a compelling set of toolswhich can be used to facilitate and accelerate AI education.

In some embodiments, graphic arts composition 1208 (e.g., drawings orpaintings) may be used as the medium for explaining various aspects ofartificial intelligence to some clients. GAI models may be trained toproduce synthetic drawings or paintings in a manner analogous to thatused for music items in such embodiments. In one embodiment, GAI modelsthat create writing 1212 (e.g., stories, poems or the like) may beemployed to help clients learn about artificial intelligence. In arelated domain, GAI techniques may also be used to speed upengineering/design tasks such as converting rough drawings or sketchedto professional level engineering drawings, which may be beneficial in avariety of applications such as automotive design, architecture and soon. In some embodiments, such engineering/design tasks may be used tohelp educate clients of an AIES.

GAI techniques introduced via sample models may also or instead be usedfor generating examples for medicine-related machine learning models1216 in at least some embodiments. In order to train models which can beused for early detection of diseases, or to predict occurrences ofdisease, high quality training data may be required. The input data forsuch models may comprise such artifacts as medical test results (whichmay be text-based, graphical, video, or multi-modal), doctor'sannotations, demographic details, and the like. Especially in the caseof relatively rare diseases, the number of actual real-world examplesavailable may be really low. Using GAI models, additional examples maybe created, in which various properties of the real-world cases may bemodified slightly. Such synthetic records may then be used to enhancethe training data sets for the models, e.g., after the synthetic recordsare approved by medical professionals. In some embodiments, specialdomain-specific versions of the AIES may be developed—e.g., a versioncreated specifically for medical professionals may be used to enhancedisease-related training data.

Video games and/or animations 1220 may represent another domain whichmay be employed for AI-related education in some embodiments. Variationsof the scenes displayed in animations or games may be generated usingsample models and the like, and clients interested in video games maythereby be attracted to explore the underlying artificial intelligencetechniques. Video games, animations and the like, as well asmedicine-related tasks and engineering/design tasks, representreal-world use cases for GAI, and training individuals on AI techniqueswhich can be used in such domains may result in a larger pool of skilledworkers who can contribute in such fields.

In some embodiments, the AIES may provide a programmatic interface whichenables a client to select the particular content type or domain (e.g.,music, drawings, text, etc.) for which sample GAI models are to bepresented and explored. The client may then learn about AI using modelsand artifacts of the chosen content type, instead of necessarily havingto use music-related models.

Methods for Hands-on AI Education

FIG. 13 is a flow diagram illustrating aspects of operations that may beperformed at an artificial intelligence education service, according toat least some embodiments. As shown in element 1301, a set of resourcesmay be configured at a network-accessible artificial intelligenceeducation service (AIES) (similar in features and functionality to AIES110 of FIG. 1 ) to enable clients to learn about various types ofmachine learning techniques, including generative AI models andalgorithms. The resources may include input record collectors forrecording and capturing input data records submitted by clients formachine learning models of the service. The input records may be createdvia simple content creation tools, such as music keyboards using MIDI(Musical Instrument Digital Interface), drawing tablets and the like.

A number of data sets or corpuses containing example content items ofdifferent types, such as music, visual arts and the like, may becollected by the AIES in some embodiments (element 1304). Metadata suchas tags which characterize the content items along various dimensions(such as artist, instruments used, year/decade created, musical orartistic genre, etc.) may be obtained from one or more sources indifferent embodiments, including for example resources of the publicInternet, groups of annotators who are familiar with the creativedomain, and so on. One or more search engines may be configured at theAIES, which may utilize indexes created on the metadata to quicklyrespond to search requests for examples based on client-selected tagsand/or descriptions of targeted content.

One or more programmatic interfaces may be presented/generated by theAIES in the depicted embodiment, enabling clients to view a set ofsample generative models and obtain (in real-time or near real-time)outputs generated by the sample models (element 1307). For example, aclient may submit digital representations of several different musiccontent items to be used as inputs for a given sample model, and beprovided representations (e.g., audio playback) of the correspondingsynthetic music content items generated by the sample model. Similarly,in at least some embodiments, upon request, a client may be provided theoutputs generated by different sample models (which may implementrespective algorithms, such as those depicted in FIG. 4 ) for a giveninput content item provided/submitted by the client, which may enablethe client to get a sense of high-level differences between thealgorithms. Optionally, graphical representations of various metricspertaining to the outputs, including metrics indicating differencesbetween pairs or larger groups of generated output items, may bedisplayed. The programmatic interfaces may include one or more web-basedportals or consoles, graphical user interfaces, command-line tools, APIsand the like.

In response to one or more programmatic requests via the programmaticinterfaces, annotated/commented model artifacts pertaining to the samplemodels (and/or other models including customized models created for aclient) may be provided (element 1310) to clients in variousembodiments. The artifacts may include, for example, representations ofmodel subcomponents (e.g., the structure of the neural networks used forgenerators, discriminators and the like in the case of GANs),hyper-parameters, source code, and the like. The client may change thestructure (e.g., the number of hidden layers of a neural network) and/orhyper-parameter values of a given model using the AIES interfaces invarious embodiments, and view/hear the resulting changes to the outputproduced by the model.

If and when a client of the AIES decides to train custom models,training data for a model type selected by a client (e.g., from amongthe types of sample models provided, such as GANs, VAEs and the like)may be identified from among the corpuses available at the AIES (element1313). Subsets of a corpus that match criteria (specified for exampleusing the tags generated or obtained earlier) may be quickly identifiedfor inclusion in a training data set using the AIES search engine insome embodiments. In some embodiments, specific tags for use as searchquery attributes may not have to be provided by the client or user;instead, for example, a client may simply provide some “good examples”of content for which similar items are to be identified from thecorpuses available, and the AIES may generate its own search queryaccordingly. A like/dislike interface of the kind discussed earlier maybe used to identify such good examples in some embodiments.

In response to one or more programmatic requests, training iterations ofa model may be started using the training data identified on behalf ofthe client (element 1316) in various embodiments. The AIES may, uponrequest, pause between iterations to display various types of metrics(similar to the subjective and/or objective metrics discussed earlier)which indicate the progress or convergence towards targeted outputcharacteristics. In at least one embodiment in which music items aregenerated, graphical representations of metrics of similarity ordifferences between pairs of outputs, produced using structuralsimilarity matrices, multi-dimension scaling and the like may beprovided.

A trained version of the model, and/or outputs produced using the modelmay be stored in a repository of the AIES in some embodiments (element1319). The stored version may be used to generate additional outputs(e.g., for new input data items, which were not in the training data)which may be distributed over networks to various destinations selectedby the client. Optionally, in some embodiments, a client may requestthat some of the generated output be published (e.g., to content sharingwebsites or the like, where the output may potentially be rated/rankedby an online audience) or entered into a competition (element 1323). TheAIES may facilitate such publishing or competition participation in atleast some embodiments, e.g., by providing simplified “one-click”interfaces for propagating the generated content.

It is noted that in various embodiments, some of the operations shown inFIG. 13 may be implemented in a different order than that shown in thefigure, or may be performed in parallel rather than sequentially.Additionally, some of the operations shown in FIG. 13 may not berequired in one or more implementations.

Use Cases

The techniques described above, of providing a network-accessibleservice user-friendly interfaces for learning about various aspects ofartificial intelligence, including generative artificial intelligence,may be useful in a variety of scenarios. As more and more differenttypes of problems are being addressed using machine learningmethodologies, larger pools of developers, data scientists and the likeare required to keep pace with demand. By utilizing the describedtechniques and interfaces, an entertaining and hands-on way ofintroducing individuals to artificial intelligence techniques,increasing such learners' confidence, and eventually enabling more rapidadvances in fields such as disease prediction, engineering design andthe like may be made possible.

Illustrative Computer System

In at least some embodiments, a server that implements one or more ofthe techniques described herein, including for example components of anartificial intelligence education service, a machine learning serviceand/or other services of a provider network and the like may include ageneral-purpose computer system that includes or is configured to accessone or more computer-accessible media. FIG. 14 illustrates such ageneral-purpose computing device 9000. In the illustrated embodiment,computing device 9000 includes one or more processors 9010 coupled to asystem memory 9020 (which may comprise both non-volatile and volatilememory modules) via an input/output (I/O) interface 9030. Computingdevice 9000 further includes a network interface 9040 coupled to I/Ointerface 9030.

In various embodiments, computing device 9000 may be a uniprocessorsystem including one processor 9010, or a multiprocessor systemincluding several processors 9010 (e.g., two, four, eight, or anothersuitable number). Processors 9010 may be any suitable processors capableof executing instructions. For example, in various embodiments,processors 9010 may be general-purpose or embedded processorsimplementing any of a variety of instruction set architectures (ISAs),such as the x86, PowerPC, SPARC, ARM or MIPS ISAs, or any other suitableISA. In multiprocessor systems, each of processors 9010 may commonly,but not necessarily, implement the same ISA. In some implementations,graphics processing units (GPUs) and or field-programmable gate arrays(FPGAs) may be used instead of, or in addition to, conventionalprocessors.

System memory 9020 may be configured to store instructions and dataaccessible by processor(s) 9010. In at least some embodiments, thesystem memory 9020 may comprise both volatile and non-volatile portions;in other embodiments, only volatile memory may be used. In variousembodiments, the volatile portion of system memory 9020 may beimplemented using any suitable memory technology, such as static randomaccess memory (SRAM), synchronous dynamic RAM or any other type ofmemory. For the non-volatile portion of system memory (which maycomprise one or more NVDIMMs, for example), in some embodimentsflash-based memory devices, including NAND-flash devices, may be used.In at least some embodiments, the non-volatile portion of the systemmemory may include a power source, such as a supercapacitor or otherpower storage device (e.g., a battery). In various embodiments,memristor based resistive random access memory (ReRAM),three-dimensional NAND technologies, Ferroelectric RAM, magnetoresistiveRAM (MRAM), or any of various types of phase change memory (PCM) may beused at least for the non-volatile portion of system memory. In theillustrated embodiment, program instructions and data implementing oneor more desired functions, such as those methods, techniques, and datadescribed above, are shown stored within system memory 9020 as code 9025and data 9026.

In one embodiment, I/O interface 9030 may be configured to coordinateI/O traffic between processor 9010, system memory 9020, and anyperipheral devices in the device, including network interface 9040 orother peripheral interfaces such as various types of persistent and/orvolatile storage devices. In some embodiments, I/O interface 9030 mayperform any necessary protocol, timing or other data transformations toconvert data signals from one component (e.g., system memory 9020) intoa format suitable for use by another component (e.g., processor 9010).In some embodiments, I/O interface 9030 may include support for devicesattached through various types of peripheral buses, such as a variant ofthe Peripheral Component Interconnect (PCI) bus standard or theUniversal Serial Bus (USB) standard, for example. In some embodiments,the function of I/O interface 9030 may be split into two or moreseparate components, such as a north bridge and a south bridge, forexample. Also, in some embodiments some or all of the functionality ofI/O interface 9030, such as an interface to system memory 9020, may beincorporated directly into processor 9010.

Network interface 9040 may be configured to allow data to be exchangedbetween computing device 9000 and other devices 9060 attached to anetwork or networks 9050, such as other computer systems or devices asillustrated in FIG. 1 through FIG. 13 , for example. In variousembodiments, network interface 9040 may support communication via anysuitable wired or wireless general data networks, such as types ofEthernet network, for example. Additionally, network interface 9040 maysupport communication via telecommunications/telephony networks such asanalog voice networks or digital fiber communications networks, viastorage area networks such as Fibre Channel SANs, or via any othersuitable type of network and/or protocol.

In some embodiments, system memory 9020 may be one embodiment of acomputer-accessible medium configured to store program instructions anddata as described above for FIG. 1 through FIG. 13 for implementingembodiments of the corresponding methods and apparatus. However, inother embodiments, program instructions and/or data may be received,sent or stored upon different types of computer-accessible media.Generally speaking, a computer-accessible medium may includenon-transitory storage media or memory media such as magnetic or opticalmedia, e.g., disk or DVD/CD coupled to computing device 9000 via I/Ointerface 9030. A non-transitory computer-accessible storage medium mayalso include any volatile or non-volatile media such as RAM (e.g. SDRAM,DDR SDRAM, RDRAM, SRAM, etc.), ROM, etc., that may be included in someembodiments of computing device 9000 as system memory 9020 or anothertype of memory. In some embodiments, a plurality of non-transitorycomputer-readable storage media may collectively store programinstructions that when executed on or across one or more processorsimplement at least a subset of the methods and techniques describedabove. A computer-accessible medium may include transmission media orsignals such as electrical, electromagnetic, or digital signals,conveyed via a communication medium such as a network and/or a wirelesslink, such as may be implemented via network interface 9040. Portions orall of multiple computing devices such as that illustrated in FIG. 14may be used to implement the described functionality in variousembodiments; for example, software components running on a variety ofdifferent devices and servers may collaborate to provide thefunctionality. In some embodiments, portions of the describedfunctionality may be implemented using storage devices, network devices,or special-purpose computer systems, in addition to or instead of beingimplemented using general-purpose computer systems. The term “computingdevice”, as used herein, refers to at least all these types of devices,and is not limited to these types of devices.

CONCLUSION

Various embodiments may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Generally speaking, acomputer-accessible medium may include storage media or memory mediasuch as magnetic or optical media, e.g., disk or DVD/CD-ROM, volatile ornon-volatile media such as RAM (e.g. SDRAM, DDR, RDRAM, SRAM, etc.),ROM, etc., as well as transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as network and/or a wireless link.

The various methods as illustrated in the Figures and described hereinrepresent exemplary embodiments of methods. The methods may beimplemented in software, hardware, or a combination thereof. The orderof method may be changed, and various elements may be added, reordered,combined, omitted, modified, etc.

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended to embrace all such modifications and changes and, accordingly,the above description to be regarded in an illustrative rather than arestrictive sense.

What is claimed is:
 1. A system, comprising: one or more computingdevices; wherein the one or more computing devices include instructionsthat upon execution on or across one or more computing devices cause theone or more computing devices to: provide, via one or more programmaticinterfaces of an artificial intelligence education service, anindication of (a) one or more sample models implementing respectivegenerative machine learning algorithms trained to create synthetic musiccontent items and (b) metadata associated with the one or more samplemodels, including respective sets of hyper-parameters of the one or moresample models; obtain, via the one or more programmatic interfaces,respective digital representations of a first input music content itemproduced by a client of the artificial intelligence education service,and a second music input content item produced by the client; cause tobe presented, via the one or more programmatic interfaces, arepresentation of (a) a first synthetic music content item, generatedusing a particular set of hyper-parameters at a particular sample modelof the one or more sample models in response to the first input musiccontent item and (b) a second synthetic music content item generated atthe particular sample model in response to the second input musiccontent item; cause to be presented, via the one or more programmaticinterfaces, an indication of a result of changing a hyper-parameter ofthe particular set of hyper-parameters, wherein the hyper-parameter ischanged in response to a request from the client; perform, usingresources of a provider network, a plurality of training iterations of agenerative machine learning model using a training data set, wherein thetraining data set is selected from a corpus of music content itemexamples of the provider network, and wherein selection of the trainingdata set is based at least in part on (a) classification metadataprovided by the provider network and (b) input obtained from the clientvia the one or more programmatic interfaces; cause to be presented, viathe one or more programmatic interfaces, at least a graphicalrepresentation of a difference between (a) a third synthetic musiccontent item produced by the generative machine learning model in aparticular training iteration of the plurality of training iterationsand (b) a fourth synthetic music content item produced by the generativemachine learning model in a different training iteration of theplurality of training iterations; store a trained version of thegenerative machine learning model; and store one or more additionalsynthetic music content items produced by the trained version.
 2. Thesystem as recited in claim 1, wherein the trained version of thegenerative machine learning model trained comprises one or more of: (a)a generative adversarial network, (b) a variational auto-encoder, (c) anauto-regressive model or (d) a transformer model.
 3. The system asrecited in claim 1, wherein the one or more computing devices includefurther instructions that upon execution on or across one or morecomputing devices further cause the one or more computing devices to:cause a notebook interface of a machine learning service of the providernetwork to be presented to the client, wherein the notebook interfaceenables annotation and exploration of a plurality of machine learningartifacts.
 4. The system as recited in claim 1, wherein the one or morecomputing devices include further instructions that upon execution on oracross one or more computing devices further cause the one or morecomputing devices to: in response to input received via the one or moreprogrammatic interfaces, modify one or more hyper-parameters of aparticular machine learning model to cause a sound of a first musicalinstrument to be substituted by the sound of a second musical instrumentin a synthetic music content item generated by the particular machinelearning model.
 5. The system as recited in claim 1, wherein the one ormore computing devices include further instructions that upon executionon or across one or more computing devices further cause the one or morecomputing devices to: cause to be presented, via the one or moreprogrammatic interfaces, a representation of a structure of a neuralnetwork of a particular generative machine learning model; and cause thestructure to be modified in response to input received via the one ormore programmatic interfaces.
 6. A computer-implemented method,comprising: providing, via one or more programmatic interfaces of aservice of a provider network, an indication of one or more samplemodels implementing respective machine learning algorithms trained tocreate synthetic content items; causing to be presented, via the one ormore programmatic interfaces, a representation of (a) a first syntheticcontent item, generated using a particular sample model of the one ormore sample models in response to a first input content item obtainedfrom a client of the provider network and (b) a second synthetic musiccontent item generated at the particular sample model in response to asecond input content item; in response to one or more messages from theclient via the one or more programmatic interfaces, selecting a trainingdata set from a corpus of content item examples of the provider network;and training, using the training data set, a machine learning model toproduce synthetic content items.
 7. The computer-implemented method asrecited in claim 6, wherein a synthetic content item produced by themachine learning model comprises one or more of: (a) a music contentitem, (b) a writing content item, (c) a synthetic example of medicaldata, or (d) an animation content item.
 8. The computer-implementedmethod as recited in claim 6, wherein the machine learning model trainedusing the training data set comprises one or more of: (a) a generativeadversarial network, (b) a variational auto-encoder, (c) anauto-regressive model or (d) a transformer model.
 9. Thecomputer-implemented method as recited in claim 6, further comprising:providing, via the one or more programmatic interfaces, an indication ofa set of hyper-parameters of a particular machine learning model trainedto generate synthetic content items; and causing to be presented, viathe one or more programmatic interfaces, an indication of a change in anoutput synthetic content item of the particular machine learning mode,wherein the change in the output synthetic content item results from achange, requested via the one or more programmatic interfaces, to aparticular hyper-parameter of the set of hyper-parameters.
 10. Thecomputer-implemented method as recited in claim 6, wherein said trainingcomprises a plurality of training iterations which include a particulariteration and a successor iteration of the particular iteration, thecomputer-implemented method further comprising: cause to be presented,via the one or more programmatic interfaces, at least a graphicalrepresentation of a difference between (a) a third synthetic contentitem produced by the machine learning model in the particular iterationand (b) a fourth synthetic content item produced by the machine learningmodel in the successor iteration, wherein a content type of at least onesynthetic content item of the third and fourth synthetic content itemsis not graphical.
 11. The computer-implemented method as recited inclaim 6, further comprising: obtaining, via the one or more programmaticinterfaces, an indication of an example content item liked by theclient; identifying, from the corpus, one or more other content itemswhich meet a similarity criterion relative to the example content item;and including the one or more other content items in the training dataset.
 12. The computer-implemented method as recited in claim 6, whereinthe first input content item is obtained from a client of the providernetwork via a music keyboard interface.
 13. The computer-implementedmethod as recited in claim 6, further comprising: obtaining, via the oneor more programmatic interfaces, feedback with respect to a particularsynthetic content item produced by a particular machine learning model;and providing, via the one or more programmatic interfaces, based atleast in part on the feedback, a recommendation for a change to ahyper-parameter of the particular machine learning model.
 14. Thecomputer-implemented method as recited in claim 6, further comprising:obtaining, via the one or more programmatic interfaces, respectiverequests to explore a plurality of generative machine learningalgorithms including a first generative machine learning algorithm and asecond generative machine learning algorithm; causing to be presented,in response to the respective requests, (a) an indication of a syntheticcontent item generated in response to a first input by the firstgenerative machine learning algorithm, and (b) an indication of adifferent synthetic content item generated in response to the firstinput by the second generative machine learning algorithm.
 15. Thecomputer-implemented method as recited in claim 6, further comprising:causing to be presented, via the one or more programmatic interfaces,indications of one or more metrics comparing a first synthetic musiccontent item produced by a particular machine learning model withanother synthetic music content item, wherein the one or more metricsincludes one or more of: (a) a structural similarity matrix, (b) anumber of musical instruments used, (c) a type of musical instrumentused, (d) a number of voices used, (e) a type of voice used, (f) apacing of notes, (g) an indication of a number of notes belonging to aparticular music genre, or (h) a percussion beat pattern.
 16. One ormore non-transitory computer-accessible storage media storing programinstructions that when executed on or across one or more processorscause the one or more processors to: provide, via one or moreprogrammatic interfaces, an indication of one or more sample modelsimplementing respective machine learning algorithms trained to createsynthetic content items; cause to be presented, via the one or moreprogrammatic interfaces, a representation of a first synthetic contentitem, generated using a particular sample model of the one or moresample models in response to a first input content item obtained from aclient of a provider network; and in response to one or more requestsreceived from the client via the one or more programmatic interfaces,train a machine learning model to produce synthetic content items. 17.The one or more non-transitory computer-accessible storage media asrecited in claim 16, wherein the machine learning model comprises one ormore of: (a) a generative adversarial network, (b) a variationalauto-encoder, (c) an auto-regressive model or (d) a transformer model.18. The one or more non-transitory computer-accessible storage media asrecited in claim 16, wherein a synthetic content item produced by themachine learning model comprises one or more of: (a) a music contentitem, (b) a writing content item, (c) a synthetic example of medicaldata, or (d) an animation content item.
 19. The one or morenon-transitory computer-accessible storage media as recited in claim 16,storing further program instructions that when executed on or across oneor more processors further cause the one or more processors to: inresponse to a programmatic request from a client, apply one or morefiltering criteria to a corpus of content items of a provider network toidentify a training data set for the machine learning model.
 20. The oneor more non-transitory computer-accessible storage media as recited inclaim 16, storing further program instructions that when executed on oracross one or more processors further cause the one or more processorsto: cause to be provided, via the one or more programmatic interfaces,respective representations of synthetic content items produced at aplurality of stages of a pipeline of sample machine learning models.